def test_vmf_aic_different_distributions():
# VMF AIC should be higher for VMF than another distribution
X = np.random.vonmises(mu=np.zeros(3), kappa=np.ones(3), size=(4, 3))
X = normalise_rows(X)
Y = np.random.dirichlet(alpha=np.random.random(3), size=4)
Y = normalise_rows(Y)
assert vmf_aic(X) > vmf_aic(Y)
def test_vmf_aic_unnormalised_matrix():
# VMF AIC is nan for unnormalised rows
X = np.array([[0.9, 0.2], [0.3, 2], [1, 1]])
with pytest.warns(RuntimeWarning):
assert np.isnan(vmf_aic(X))
# Normalising fixes this
norm_X = X / np.linalg.norm(X, axis=1)[:, np.newaxis]
assert np.isfinite(vmf_aic(norm_X))
def test_vmf_aic_zero_matrix(X):
with pytest.warns(RuntimeWarning):
assert np.isnan(vmf_aic(X))
def test_vmf_aic_num(X, Y):
if str(Y) == "NaN":
assert np.isnan(vmf_aic(X))
else:
assert np.allclose(vmf_aic(X), Y, atol=1e-8)
def test_vmf_aic(X):
assert np.isfinite(vmf_aic(X))